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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Tractography gone wild: probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI.

Derek K Jones1

  • 1Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, CF10 3AT Cardiff, U.K. jonesd27@cf.ac.uk

IEEE Transactions on Medical Imaging
|September 10, 2008
PubMed
Summary

This study introduces a faster method for assigning confidence to white matter tract reconstructions using diffusion tensor magnetic resonance imaging (DT-MRI). The wild bootstrap technique enables reliable pathway confidence with significantly reduced scan times.

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • Diffusion tensor magnetic resonance imaging (DT-MRI) enables noninvasive assessment of tissue microstructure.
  • Fibre-tracking algorithms reconstruct 3-D white matter trajectories, but assigning confidence to these pathways is challenging.
  • Probabilistic algorithms often rely on a priori assumptions, while traditional bootstrap methods require prohibitive acquisition times.

Purpose of the Study:

  • To develop a computationally efficient method for assigning confidence to DT-MRI tractography reconstructions.
  • To validate the use of the wild bootstrap method in combination with tractography for assessing pathway uncertainty.
  • To enable retrospective confidence assignment for existing DT-MRI datasets.

Main Methods:

  • The study combined the wild bootstrap method with established tractography techniques.
  • In vivo DT-MRI data were acquired and analyzed using the proposed wild bootstrap tracking.
  • Results were compared against traditional bootstrapping and deterministic tracking methods.

Main Results:

  • The wild bootstrap approach successfully assigned confidence to reconstructed white matter trajectories.
  • Acquisition times were a fraction of those required for regular bootstrapping.
  • In vivo wild bootstrap tracking results demonstrated comparability with regular tracking outcomes.

Conclusions:

  • Combining the wild bootstrap with tractography offers a time-efficient solution for assigning confidence to DT-MRI pathways.
  • This method allows researchers to retrospectively assign confidence to reconstructed trajectories with minimal additional effort.
  • The approach is applicable to datasets acquired for deterministic tracking, broadening the utility of bootstrap analyses.